1. Field of the Invention
The present invention relates to a noise reduction processing method for a biological tissue image and an apparatus therefor. Specifically, the present invention relates to a method and apparatus for reconstructing a biological tissue image having reduced noise components from measured spectrum data of a biological tissue. The present invention also relates to an image display for clearly displaying a diseased site in pathological diagnosis through use of the thus acquired biological tissue image.
2. Description of the Related Art
There has been performed pathological diagnosis, that is, observing a biological tissue with a microscope or the like and diagnosing the presence or absence of a lesion and a type of the lesion based on the observation. The pathological diagnosis requires visualization of a constituent substance or contained substance correlated with a biological tissue to be observed. A technique for staining a specific antigen protein through use of an immunostaining method has mainly been employed in the pathological diagnosis. When breast cancer is taken as an example, an estrogen receptor (ER) (serving as a judgment criterion for a hormone therapy), which is expressed in a hormone-dependent tumor, and a membrane protein HER2 (serving as a judgment criterion for Herceptin administration), which is found in a fast-growing malignant cancer, are visualized by the immunostaining method. However, the immunostaining method involves problems that its reproducibility is poor because an antibody is unstable and antigen-antibody reaction efficiency is difficult to control. Further, in the future, when there is an increasing need for such functional diagnosis, for example, when there arises a need for detection of several tens or more kinds of constituent substances or contained substances, currently-employed immunostaining methods cannot meet the need any more.
Further, in some cases, the visualization of the constituent substance or contained substance may be required at a cellular level, not at a tissue level. For example, in research on cancer stem cells, it was revealed that a tumor was formed in only part of fractions of a tumor tissue after xenotransplantation to immunocompromised mice. Therefore, it is being understood that growth of a tumor tissue, in which cancer stem cells are recognized, depends on differentiation and self-renewal abilities of the cancer stem cells. In such research, it is necessary to observe an expression distribution of a constituent substance or contained substance in an individual cell in a tissue, not the entire tissue.
As described above, in the pathological diagnosis, a constituent substance or contained substance correlated with a tumor tissue or the like is required to be exhaustively visualized at a cellular level. There are given, as candidates of a method for the visualization, secondary ion mass spectrometry (SIMS), such as time-of-flight secondary ion mass spectrometry (TOF-SIMS), and Raman spectroscopy. In measurement by the SIMS or Raman spectroscopy, information at each point (region) in a space can be obtained with a high spatial resolution. That is, spatial distribution information on each peak value for a measured spectrum correlated with an object to be measured is obtained. Consequently, a spatial distribution of a substance in a biological tissue correlated with the measured spectrum can be determined.
The SIMS is a method involving irradiating a sample with a primary ion beam, and detecting a secondary ion emitted from the sample, thereby obtaining a mass spectrum at each point on the sample. For example, in TOF-SIMS, through utilization of the fact that a time-of-flight of a secondary ion depends on a mass m and charge z of the ion, the secondary ion is identified, and thereby a mass spectrum at each point on a sample can be obtained.
The Raman spectroscopy involves acquiring a Raman spectrum by irradiating a substance with a laser beam, which is monochromatic light, as a light source, and detecting the generated Raman scattered light with a spectrometer or an interferometer. A difference (Raman shift) between a frequency of the Raman scattered light and a frequency of incident light has a value peculiar to a structure of a substance. Hence, a Raman spectrum specific for an object to be measured can be acquired.
As used herein, the “cellular level” means a level at which at least an individual cell can be identified. A diameter of the cell falls within a range of approximately 10 μm to 20 μm (except that a large cell such as a nerve cell has a diameter of about 50 μm). Thus, in order to acquire a two-dimensional distribution image at a cellular level, the spatial resolution needs to be 10 μm or less, preferably 5 μm or less, more preferably 2 μm or less, still more preferably 1 μm or less. The spatial resolution may be determined from, for example, results of linear analysis of a knife-edge sample. That is, the spatial resolution is determined based on the following general definition: “a distance between two points at which signal intensities attributed to a substance of interest near the boundary of a sample are 20% and 80%, respectively.”
In order to acquire biological information from measured spectrum, for example, it is necessary to generate a classifier by machine learning in advance and to apply the classifier to measured spectrum data of a sample (Japanese Patent Application Laid-Open No. 2010-71953). However, when its signal intensity is low, it is impossible to disregard influences of noise components on the classification processing. Hence, it is necessary to appropriately reduce noise components each having a low correlation with an original signal of a biological tissue. As used herein, machine learning refers to a technique involving empirically learning previously acquired data, and interpreting newly acquired data based on the learning results. The classifier refers to judgment criterion information to be generated by empirically learning a relationship between previously acquired data and biological information.
Various noise reduction techniques are known. Japanese Patent Application Laid-Open No. 2007-209755 proposes a technique for reducing noise effectively by analyzing two or more two-dimensional images through use of wavelet analysis, and considering a correlation between both the images. S. G. Nikolov et al., “De-noising of SIMS images via wavelet shrinkage,” Chemometrics and Intelligent Laboratory Systems, vol. 34 (1996), p. 263-273 proposes a noise reduction technique in consideration of a probability process (Gauss or Poisson process) involving using two-dimensional wavelet analysis for an SIMS image.